Sarcoidosis is a disease of unknown cause afflicting tens of thousands of patients in the USA alone. The presentation of the disease is highly variable, ranging from clinically imperceptible disease to life-threatening complications caused by extensive granuloma formation in the lungs and/or other vital organs. Many cases of sarcoidosis are sporadic, while others cluster within families and racial groups, implying a genetic predilection for disease. Recent investigations support a link between genetic variables and sarcoidosis. For instance, nucleotide polymorphisms in close proximity to genes regulating the initiation and perpetuation of inflammation are strongly associated with sarcoidosis. However, genome-wide mutation analyses are of low resolution, potentially missing many genes that may contribute to disease pathogenesis, while identifying others that are irrelevant. By contrast, DNA expression analyses are of relatively high resolution (>12,000 genes), can be performed on tissue samples to provide gene expression patterns at the site of disease activity, and quantitative feedback in terms of gene over- and under-expression. As such, genes that are likely to promote disease may be distinguished from those that modulate disease activity. Therefore, this proposal will analyze gene expression patterns in diseased tissues of patients with sarcoidosis to accomplish the following aims: Aim 1: To Identify Gene Expression Patterns That Specifically Correlate with Active Pulmonary Sarcoidosis. Aim 2: To Determine If Gene Expression Patterns Correlate With The Severity of Illness Of Sarcoidosis Patients. Aim 3: To Determine If Gene Expression Patterns Will Differentiate Granuloma Caused By Sarcoidosis From Those Caused by Histoplasmosis. Gene expression patterns will be generated from total RNA derived from tissue samples of adults with newly diagnosed sarcoidosis, matching normal tissue, and tissues of patients with histoplasmosis, a granulomatous disease that is clinically similar to sarcoidosis. Statistical comparisons of gene expression patterns will then be performed. Novel insights relating gene profiles to disease manifestation, prognosis and pathogenesis are likely outcomes of these investigations.